Predict for $\mathit{m}\mathit{A}{\mathit{b}}_{\mathit{i}}$. Ranked list of candidate epitope positions on the HA antigen.  Input:  • $\mathit{P}\left(\mathit{m}{\mathit{A}\mathit{b}}_{\mathit{i}}\right)=P\left({mAb}_i^1,{mAb}_i^2,\cdots ,{mAb}_i^N\right)$: mAb $i$ binding profile across a set of $j=1\dots N$ HA antigens from a given group.  • $\mathit{P}\left(\mathit{m}\mathit{A}{\mathit{b}}_{\mathit{s}\mathit{e}\mathit{t}}\right)$: binding profiles for a set of mAbs with known binding region (head or stalk) across the set of $N$ HA strains.  • $\mathit{A}$: multiple sequence alignment of the set of $N$ HA antigen sequences.  • $\mathit{t},\mathit{k},\mathit{Q}$: given parameters where $\mathit{t}$ is a given binding threshold, $\mathit{k}$ is given parameter to KNN classification, and $\mathit{Q}$ is seed epitope patch length.  (1) Classify binding region (head vs. stalk) of $mA{b}_i$ using KNN classification over $\mathit{P}\left(\mathit{m}\mathit{A}{\mathit{b}}_{\mathit{s}\mathit{e}\mathit{t}}\right)$.  (2) Binarize $\mathit{P}\left(\mathit{m}\mathit{A}{\mathit{b}}_{\mathit{i}}\right)$ using a given binding threshold $t$: ${\mathit{P}}_{\mathit{b}\mathit{i}\mathit{n}\mathit{a}\mathit{r}\mathit{i}\mathit{z}\mathit{e}\mathit{d}}\left(\mathit{m}\mathit{A}{\mathit{b}}_{\mathit{i}}^{\mathit{j}}\right)=\left\{\begin{array}{c}1ifP\left(mA{b}_i^j\right)>t\\ 0otherwise\dots \dots ..\end{array}\right\}$.  (3) Define the set of binding HA strains ${HA}_{bound}=\left\{{HA}^j|P_{binarized}\left(mA{b}_i^j\right)=1\right\}$ and the set of non-binding strains ${HA}_{unbound}=\left\{{HA}^j|P_{binarized}\left(mA{b}_i^j\right)=0\right\}$.  (4) Compute position $a$-specific score, $S\left(a\right)$, over $A$ for all positions within the predicted binding region using the following formula:  $\mathit{S}\left(\mathit{a}\right)=\mathbf{1}-\frac{{\mathit{S}}_{\mathit{w}}\left(\mathit{a}\right)}{{\mathit{S}}_{\mathit{b}}\left(\mathit{a}\right)}\text{,}$  where ${\mathit{S}}_{\mathit{w}}\left(\mathit{a}\right)=\frac{1}{N_w}\sum _{m,n\in {HA}_{bound}}{D}_{BLOSUM62}\left(a^n,a^m\right)\times \frac{\left(P\left(mA{b}_i^n\right)+P\left(mA{b}_i^m\right)\right)}{2}$, and ${\mathit{S}}_{\mathit{b}}\left(\mathit{a}\right)=\frac{1}{N_b}\sum _{n\in {HA}_{bound},m\in {HA}_{unbound}}{D}_{BLOSUM62}(a^n,a^m)\times \frac{\left(P\left(mA{b}_i^n\right)+P\left(mA{b}_i^m\right)\right)}{2}\text{.}$  ${\mathit{N}}_{\mathit{w}}$: number of pairs of sequences of binding strains in ${HA}_{bound}$. ${\mathit{N}}_{\mathit{b}}$: the number of binding sequences in ${HA}_{bound}$ times the number of non-binding sequences in ${HA}_{unbound}$. ${\mathit{D}}_{\mathit{B}\mathit{L}\mathit{O}\mathit{S}\mathit{U}\mathit{M}\mathbf{62}}\left({\mathit{a}}^{\mathit{X}},{\mathit{a}}^{\mathit{Y}}\right)$: a modified BLOSUM62 distance measure (see STAR Methods) between amino acid in position $a$ from sequence $X$ and sequence $Y$ in $A$.  (5) Rank all positions on the HA region and select the top $Q$ positions as the seed epitope patch, $\mathit{S}\mathit{E}\mathit{P}$.  (6) Rank all non-$\mathit{S}\mathit{E}\mathit{P}$ remaining positions on the HA binding region using the geometric mean distance to the $\mathit{S}\mathit{E}\mathit{P}$ center to obtain final ranked list of all HA binding region positions.  Output: ranked list of candidate epitope positions within the HA binding region.